🎓 Student Performance Analysis & Prediction using Machine Learning

📌 Project Overview

This project analyzes how demographic and socio-economic factors influence student academic performance and builds machine learning models to predict student math scores.

The project combines Exploratory Data Analysis (EDA) and Machine Learning to extract insights and create predictive models that can help educators identify at-risk students early.

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🎯 Problem Statement

Educational institutions often struggle to:

• Identify students who may perform poorly
• Understand factors affecting academic success
• Provide early intervention

This project uses data analysis and machine learning to uncover patterns and predict student performance.

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📊 Dataset Description

The dataset contains student demographic and academic information:

Feature	Description
Gender	Male/Female
Race/Ethnicity	Group A–E
Parental Level of Education	Parent education background
Lunch	Standard / Free or Reduced
Test Preparation Course	Completed / None
Math Score	Target variable 🎯
Reading Score	Exam score
Writing Score	Exam score

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🔎 Exploratory Data Analysis (EDA)

Key steps performed:

✔ Data cleaning & preprocessing
✔ Missing value and duplicate checks
✔ Feature engineering
✔ Distribution & correlation analysis
✔ Data visualization

📈 Feature Engineering

New features created:

• Total Score = Math + Reading + Writing
• Average Score

These features improved the understanding of overall performance.

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📊 Key Insights from Data Analysis

• Students who completed test preparation courses scored higher.
• Students with standard lunch performed better.
• Parental education level strongly affects performance.
• Noticeable performance patterns across gender and ethnicity.
• Strong correlation between reading, writing, and math scores.

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🤖 Machine Learning Models Used

Multiple regression models were trained and compared:

• Linear Regression ⭐ (Best model)
• Random Forest Regressor
• Decision Tree Regressor
• K-Nearest Neighbors Regressor

🏆 Best Model: Linear Regression

Linear Regression achieved the best performance in predicting math scores.

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📏 Evaluation Metrics

Models were evaluated using:

• R² Score
• Mean Absolute Error (MAE)
• Root Mean Squared Error (RMSE)

These metrics measure prediction accuracy and model reliability.

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🛠 Tech Stack

Programming Language

• Python 🐍

Libraries

• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook

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📁 Project Structure